The drive by semiconductor manufacturers to smaller feature sizes with improved yield in semiconductor fabrication and Liquid Crystal Display (LCD) fabrication places greater challenges on identifying and resolving defects on surface components. For example, defect detection, classification, and effective use of defect data are essential for yield learning during ramp-up and for process control during production. The challenge of improving and maintaining yields continues to grow as device sizes become smaller, the number of surface components on each wafer increase, and the structure of circuit designs becomes more complex on more layers.
While particulate contamination limits yield, as design rules go below 0.35 mm, process defects do at least as much to limit yields. Differentiating critical defects from nuisance defects, identifying process problems and predicting yield impacts all need to be assessed in near real time to prevent yield busts or yield catastrophes. Semiconductor manufacturing requires improved defect detection, classification, and data handling technologies to meet the increasing standards. Types of macro and micro defects include: Gray Spot, Gray Streak, Gray Spot and Gray Streak, Particles, Multi-Layer Structure, Line Break, Subsurface Line, Scratch, Hillocks, Grass, Worm-hole, Starburst, Speedboat, Orange Peel, Resist Gel Defect, Controlled Collapse Chip Connection (C4), Microbridge, Submicron, Micron, Micron Sphere, U. Pattern, Contamination, Protrusion, Break, Intrusion, Nuisance, Mask-Related (Shorts), Haze, Micro-contamination, Crystalline (Stacking Fault), Spots, Break, Recticle, Hard-Defects (Pinholes, Pindots, Extrusions), Semi-Transparent (Resist Residues, Thin Chrome), Registration (Oversized, Undersized, Mislocated), Corner, Extra Metal, Metal Missing and Opens (Pattern Missing).
Prior art defect detection in a wafer pattern involved acquisition of a wafer image and two reference images at corresponding surface component locations to the left and right of or above and below the image. A detection system aligned the images by searching for a maximum correlation coefficient between images. A detection system then compared the image with each of the two reference images, using pixel by pixel subtraction, to arrive at two difference images. Theoretically, a true defect should exist at the same location of both difference images. If the value of both difference images is large enough when compared to a threshold, the system considers a true defect to exist at that location.
However, many potential false defects may be generated using this method. For example, background color variations caused by chemical mechanical polishing (CMP) have no effect on yield, yet may trigger a difference threshold when using the difference approach. In addition, this method is sensitive to noise caused by factors such as metal grain structure and imperfect alignment. Furthermore, the traditional detection method does not perform- defect classification. Therefore, classification results cannot be used as feedback to refine the detection outcomes and therefore do not effectively discriminate between critical defects and nuisance defects.